Integration of genome-wide association studies and gene coexpression networks unveils promising soybean resistance genes against five common fungal pathogens

Transcriptome Comparison between Resistant and Susceptible Soybean Cultivars in Response to Inoculation of Phytophthora sojae

Phytophthora root and stem rot, caused by Phytophthora sojae, considerably reduces soybean yield worldwide. Our previous study identified two genomic regions on chromosome 18 (2.1-2.6 and 53.1-53.3 Mbp) that confer resistance to the P. sojae isolate 2457, through linkage analysis using progenies derived from the Daepung × Socheong2 population. These two regions contained 51 and 19 annotated genes, respectively. However, the specific gene responsible for resistance to P. sojae isolate 2457 has yet to be identified. In this study, we performed a comparative transcriptomic analysis of Socheong2 and Daepung, two Korean soybean varieties identified as resistant and susceptible to P. sojae isolate 2457, respectively. RNA sequencing was conducted on tissue samples collected at 0, 6, and 12 hours after inoculation (HAI), and significant differences in the expression of defense-related genes were observed across time points and between the two cultivars. Genes associated with the jasmonic acid, salicylic acid, ethylene, and systemic acquired resistance pathways were upregulated in both cultivars at 6 and 12 HAI compared to 0 HAI, with these biological processes were more strongly upregulated in Socheong2 compared to Daepung at 6 and 12 HAI. A comparison of differentially expressed genes (DEGs) and candidate genes within the previously identified QTL regions revealed an ortholog of the HS1 PRO-1 2 gene from Arabidopsis thaliana among the upregulated DEGs in Socheong2, particularly at 12 HAI compared to 0 HAI. This study will aid in targeted breeding efforts to develop soybean varieties with improved resistance to P. sojae.

Genome-wide association study revealed some new candidate genes associated with flowering and maturity time of soybean in Central and West Siberian regions of Russia

The duration of flowering and maturity is an important agricultural trait determining the suitability of a variety for cultivation in the target region. In the present study, we used genome-wide association analysis (GWAS) to search for loci associated with soybean flowering and maturity in the Central and West Siberian regions of Russia. A field experiment was conducted in 2021/2022 at two locations (Orel and Novosibirsk). A germplasm collection of 180 accessions was genotyped using SoySNP50K Illumina Infinium Bead-Chip. From the initial collection, we selected 129 unrelated accessions and conducted GWAS on this dataset using two multi-locus models: FarmCPU and BLINK. As a result, we identified 13 loci previously reported to be associated with duration of soybean development, and 17 new loci. 33 candidate genes were detected in these loci using analysis of co-expression, gene ontology, and literature data, with the best candidates being Glyma.03G177500, Glyma.13G177400, and Glyma.06G213100. These candidate genes code the Arabidopis orthologs TOE1 (TARGET OF EAT 1), SPL3 (SQUAMOSA PROMOTER BINDING PROTEIN LIKE 3), the DELLA protein, respectively. In these three genes, we found haplotypes which may be associated with the length of soybean flowering and maturity, providing soybean adaptation to a northern latitudes.

No Common Candidate Genes for Resistance to Fusarium graminearum, F. proliferatum, F. sporotrichioides, and F. subglutinans in Soybean Accessions from Maturity Groups 0 and I: Findings from Genome-wide Association Mapping

Seedling diseases and root rot, caused by species of Fusarium, can limit soybean (Glycine max L.) production in the United States. Currently, there are few commercially available cultivars resistant to Fusarium. This study was conducted to assess the resistance of soybean maturity group (MG) accessions from 0 and I to Fusarium proliferatum, F. sporotrichioides, and F. subglutinans, as well as to identify common quantitative trait loci (QTLs) for resistance to these pathogens, in addition to F. graminearum, using a genome-wide association study (GWAS). A total of 155, 91, and 48 accessions from the United States Department of Agriculture (USDA) soybean germplasm collection from MG 0 and I were screened with a single isolate each of F. proliferatum, F. sporotrichioides, and F. subglutinans, respectively, using the inoculum layer inoculation method in the greenhouse. The disease severity was assessed 21 days postinoculation and analyzed using nonparametric statistics to determine the relative treatment effects (RTEs). Eleven and seven accessions showed significantly lower RTEs when inoculated with F. proliferatum and F. subglutinans, respectively, compared with the susceptible cultivar 'Williams 82'. One accession was significantly less susceptible to both F. proliferatum and F. subglutinans. The GWAS conducted with 41,985 single-nucleotide markers identified one QTL associated with resistance to both F. proliferatum and F. sporotrichioides, as well as another QTL for resistance to both F. subglutinans and F. graminearum. However, no common QTLs were identified for the four pathogens. The USDA accessions and QTLs identified in this study can be utilized to selectively breed resistance to multiple species of Fusarium.[Formula: see text] Copyright © 2024 The Author(s). This is an open access article distributed under the CC BY 4.0 International license.

Pathogenicity, Host Resistance, and Genetic Diversity of Fusarium Species under Controlled Conditions from Soybean in Canada

Fusarium spp. are commonly associated with the root rot complex of soybean (Glycine max). Previous surveys identified six common Fusarium species from Manitoba, including F. oxysporum, F. redolens, F. graminearum, F. solani, F. avenaceum, and F. acuminatum. This study aimed to determine their pathogenicity, assess host resistance, and evaluate the genetic diversity of Fusarium spp. isolated from Canada. The pathogenicity of these species was tested on two soybean cultivars, 'Akras' (moderately resistant) and 'B150Y1' (susceptible), under greenhouse conditions. The aggressiveness of the fungal isolates varied, with root rot severities ranging from 1.5 to 3.3 on a 0-4 scale. Subsequently, the six species were used to screen a panel of 20 Canadian soybean cultivars for resistance in a greenhouse. Cluster and principal component analyses were conducted based on the same traits used in the pathogenicity study. Two cultivars, 'P15T46R2' and 'B150Y1', were consistently found to be tolerant to F. oxysporum, F. redolens, F. graminearum, and F. solani. To investigate the incidence and prevalence of Fusarium spp. in Canada, fungi were isolated from 106 soybean fields surveyed across Manitoba, Saskatchewan, Ontario, and Quebec. Eighty-three Fusarium isolates were evaluated based on morphology and with multiple PCR primers, and phylogenetic analyses indicated their diversity across the major soybean production regions of Canada. Overall, this study contributes valuable insights into host resistance and the pathogenicity and genetic diversity of Fusarium spp. in Canadian soybean fields.

RicePilaf: a post-GWAS/QTL dashboard to integrate pangenomic, coexpression, regulatory, epigenomic, ontology, pathway, and text-mining information to provide functional insights into rice QTLs and GWAS loci

BACKGROUND

As the number of genome-wide association study (GWAS) and quantitative trait locus (QTL) mappings in rice continues to grow, so does the already long list of genomic loci associated with important agronomic traits. Typically, loci implicated by GWAS/QTL analysis contain tens to hundreds to thousands of single-nucleotide polmorphisms (SNPs)/genes, not all of which are causal and many of which are in noncoding regions. Unraveling the biological mechanisms that tie the GWAS regions and QTLs to the trait of interest is challenging, especially since it requires collating functional genomics information about the loci from multiple, disparate data sources.

RESULTS

We present RicePilaf, a web app for post-GWAS/QTL analysis, that performs a slew of novel bioinformatics analyses to cross-reference GWAS results and QTL mappings with a host of publicly available rice databases. In particular, it integrates (i) pangenomic information from high-quality genome builds of multiple rice varieties, (ii) coexpression information from genome-scale coexpression networks, (iii) ontology and pathway information, (iv) regulatory information from rice transcription factor databases, (v) epigenomic information from multiple high-throughput epigenetic experiments, and (vi) text-mining information extracted from scientific abstracts linking genes and traits. We demonstrate the utility of RicePilaf by applying it to analyze GWAS peaks of preharvest sprouting and genes underlying yield-under-drought QTLs.

CONCLUSIONS

RicePilaf enables rice scientists and breeders to shed functional light on their GWAS regions and QTLs, and it provides them with a means to prioritize SNPs/genes for further experiments. The source code, a Docker image, and a demo version of RicePilaf are publicly available at https://github.com/bioinfodlsu/rice-pilaf.

The Soybean Expression Atlas v2: A comprehensive database of over 5000 RNA-seq samples

Soybean is a crucial crop worldwide, used as a source of food, feed, and industrial products due to its high protein and oil content. Previously, the rapid accumulation of soybean RNA-seq data in public databases and the computational challenges of processing raw RNA-seq data motivated us to develop the Soybean Expression Atlas, a gene expression database of over a thousand RNA-seq samples. Over the past few years, our database has allowed researchers to explore the expression profiles of important gene families, discover genes associated with agronomic traits, and understand the transcriptional dynamics of cellular processes. Here, we present the Soybean Expression Atlas v2, an updated version of our database with a fourfold increase in the number of samples, featuring transcript- and gene-level transcript abundance matrices for 5481 publicly available RNA-seq samples. New features in our database include the availability of transcript-level abundance estimates and equivalence classes to explore differential transcript usage, abundance estimates in bias-corrected counts to increase the accuracy of differential gene expression analyses, a new web interface with improved data visualization and user experience, and a reproducible and scalable pipeline available as an R package. The Soybean Expression Atlas v2 is available at https://soyatlas.venanciogroup.uenf.br/, and it will accelerate soybean research, empowering researchers with high-quality and easily accessible gene expression data.

Polygenic pathogen networks influence transcriptional plasticity in the Arabidopsis-Botrytis pathosystem

Bidirectional flow of information shapes the outcome of the host-pathogen interactions and depends on the genetics of each organism. Recent work has begun to use co-transcriptomic studies to shed light on this bidirectional flow, but it is unclear how plastic the co-transcriptome is in response to genetic variation in both the host and pathogen. To study co-transcriptome plasticity, we conducted transcriptomics using natural genetic variation in the pathogen, Botrytis cinerea, and large-effect genetic variation abolishing defense signaling pathways within the host, Arabidopsis thaliana. We show that genetic variation in the pathogen has a greater influence on the co-transcriptome than mutations that abolish defense signaling pathways in the host. Genome-wide association mapping using the pathogens' genetic variation and both organisms' transcriptomes allowed an assessment of how the pathogen modulates plasticity in response to the host. This showed that the differences in both organism's responses were linked to trans-expression quantitative trait loci (eQTL) hotspots within the pathogen's genome. These hotspots control gene sets in either the host or pathogen and show differential allele sensitivity to the host's genetic variation rather than qualitative host specificity. Interestingly, nearly all the trans-eQTL hotspots were unique to the host or pathogen transcriptomes. In this system of differential plasticity, the pathogen mediates the shift in the co-transcriptome more than the host.

Discovering and prioritizing candidate resistance genes against soybean pests by integrating GWAS and gene coexpression networks

Soybean is one of the most important legume crops worldwide. Soybean pests have a considerable impact on crop yield. Here, we integrated publicly available genome-wide association studies and transcriptomic data to prioritize candidate resistance genes against the insects Aphis glycines and Spodoptera litura, and the nematode Heterodera glycines. We identified 171, 7, and 228 high-confidence candidate resistance genes against A. glycines, S. litura, and H. glycines, respectively. We found some overlap of candidate genes between insect species, but not between insects and H. glycines. Although 15% of the prioritized candidate genes encode proteins of unknown function, the vast majority of the candidates are related to plant immunity processes, such as transcriptional regulation, signaling, oxidative stress, recognition, and physical defense. Based on the number of resistance alleles, we selected the ten most promising accessions against each pest species in the soybean USDA germplasm. The most resistant accessions do not reach the maximum theoretical resistance potential, indicating that they might be further improved to increase resistance in breeding programs or through genetic engineering. Finally, the coexpression networks we inferred in this work are available in a user-friendly web application (https://soypestgcn.venanciogroup.uenf.br/) and an R/Shiny package (https://github.com/almeidasilvaf/SoyPestGCN) that serve as a public resource to explore soybean-pest interactions at the transcriptional level.